Complementary Fusion of Deep Network and Tree Model for ETA Prediction

TL;DR

This paper introduces a novel ensemble approach combining deep neural networks and tree models for improved ETA prediction, demonstrating superior accuracy and robustness in transportation systems.

Contribution

It presents a new ensemble method that fuses deep networks with tree models specifically for ETA prediction, achieving state-of-the-art results.

Findings

Won first place in SIGSPATIAL 2021 GISCUP competition.

Proved the ensemble method's accuracy and robustness.

Outperformed existing ETA prediction models.

Abstract

Estimated time of arrival (ETA) is a very important factor in the transportation system. It has attracted increasing attentions and has been widely used as a basic service in navigation systems and intelligent transportation systems. In this paper, we propose a novel solution to the ETA estimation problem, which is an ensemble on tree models and neural networks. We proved the accuracy and robustness of the solution on the A/B list and finally won first place in the SIGSPATIAL 2021 GISCUP competition.